How to map X to Y for TensorFlow RNN training data
I found out how to get 3D output from LSTMCell so that I can matmul with output weights + biases and subtract with expected values:


Inputs &amp; expecteds should be: placeholder(,[times,batch_size,num_inp]) instead of batch_size first then times. However, tf.keras.layers.LSTM will ask for [batch_size,times,num_inp]
Use tf.nn.static_rnn with a list of inputs, instead of 1 input


Source code:

import tensorflow as tf;

x = [[[1],[2],[3]],[[4],[5],[6]]];
times = 2;
bsize = 3;

#3d input
inputs = tf.placeholder(tf.float32, [times,bsize,1]);

cell   = tf.nn.rnn_cell.BasicRNNCell(20);
hids,_ = tf.nn.static_rnn(cell,tf.unstack(inputs,times),dtype=tf.float32);

sess = tf.Session();
init = tf.global_variables_initializer();
sess.run(init);

#results in 2d
print(sess.run(hids, {inputs:x}));